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Central Station's intended use is to retrospectively receive; display and store monitored vital signs parameters and related data. Central Station displays the data and system alerts for review and evaluation by a healthcare professional. Central Station is not intended for emergency use or real-time monitoring.

Central Station is a software system that operates on a commercially available PC system with the minimum performance specifications consistent with typical PC hardware and equipment specifications. Central Station accepts data from Honeywell HomMed Patient Monitors (e.g. Sentry and Genesis) as well as the Honeywell HomMed MedPartner.

The provided text describes a 510(k) submission for the Honeywell HomMed Central Station version 4.0. It primarily focuses on the device's intended use and the general compliance of its software. However, it does not contain detailed acceptance criteria, specific device performance metrics, or a study that rigorously proves the device meets such criteria in a quantitative sense as typically expected for medical device performance evaluation (e.g., accuracy, sensitivity, specificity).

Here's an analysis based on the provided text, highlighting what is present and what is missing:

1. A table of acceptance criteria and the reported device performance

Missing Information: The document does not provide quantitative acceptance criteria (e.g., specific accuracy thresholds, uptime requirements, response times, or error rates for display/storage) or specific performance metrics that would typically be a result of a direct performance study. The reported "performance" is a high-level statement of compliance with general expectations.

2. Sample sized used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

• Sample Size for Test Set: Not specified.
• Data Provenance: Not specified. The data refers to "monitored vital signs parameters and related data" but there is no information on the origin or nature of this data used for validation. The device's intended use is to "retrospectively receive; display and store" data, which implies it handles retrospective data, but this doesn't clarify the nature of data used for testing its own performance.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)

• Number of Experts: Not applicable. The device is a "Patient Vital Signs Monitor Viewing Station" and its validation focused on software compliance, specifications, and functional requirements. It does not involve interpretation tasks where expert ground truth would be established for diagnostic accuracy, for example. The "review and evaluation by a healthcare professional" refers to the intended use of the displayed data, not the validation of the device itself by experts.
• Qualifications of Experts: Not applicable.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set

• Adjudication Method: Not applicable. The validation described is for software compliance and functionality, not for diagnostic or interpretive performance requiring adjudication of expert opinions.

5. If a multi reader multi case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance

• MRMC Study: No, an MRMC study was not done.
• Effect Size of Improvement: Not applicable. This device is not an AI-assisted diagnostic tool; it is a data display and storage system.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done

• Standalone Performance: The description of "software validation results demonstrated that the Central Station System was in compliance with the guidelines and standards ... and that it performed within its specifications and functional requirements for software" implies a standalone evaluation of the software's ability to process, display, and store data. However, it's not "algorithm only" in the sense of a predictive or diagnostic algorithm. It's a functional software system.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)

• Type of Ground Truth: Not explicitly stated as "ground truth" in a clinical sense. For software validation, the "ground truth" would be the expected behavior or outcome based on the software's specifications and requirements. For example, if a vital sign reading is X, the "ground truth" for the display function is that it should display X correctly. The document only mentions that the software "performed within its specifications and functional requirements," implying that its output was compared against its design requirements for proper functioning.

8. The sample size for the training set

• Sample Size for Training Set: Not applicable. This is not a machine learning or AI device that requires a training set. It is a software system for data display and storage.

9. How the ground truth for the training set was established

• How Ground Truth for Training Set was Established: Not applicable, as there is no training set for this type of device.

In summary: The provided 510(k) summary focuses on the regulatory compliance and general functional validation of a software system designed to receive, display, and store vital sign data. It explicitly states that "the software validation results demonstrated that the Central Station System was in compliance with the guidelines and standards referenced in the FDA reviewer's guides and that it performed within its specifications and functional requirements for software." This type of submission relies on demonstrating that the software meets its pre-defined design and functional requirements rather than clinical performance metrics often associated with diagnostic or AI-powered devices. The absence of quantitative performance data, expert reviews, or MRMC studies is consistent with the nature of this device as a data viewing station rather than a diagnostic tool.

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Central Station's intended use is to retrospectively receive, display and store monitored vital signs parameters and related data. The Central Station displays the data and system alerts for review and interpretation by a healthcare professional. Central Station is not intended for emergency use or real-time monitoring.

Central Station is a software system that operates on a commercially available PC system with the minimum performance specifications consistent with typical PC hardware and equipment specifications. Central Station accepts data from Honeywell HomMed Patient Monitors (Sentry and Genesis) as well as the Honeywell HomMed MedPartner.

The provided text describes the Honeywell HomMed Central Station, Version 3.5, a software system for retrospectively receiving, displaying, and storing monitored vital signs parameters and related data.

Here's an analysis of the acceptance criteria and study information based on the provided document:

1. Table of Acceptance Criteria and Reported Device Performance

2. Sample Size Used for the Test Set and Data Provenance

The document does not explicitly state a specific "test set" in the traditional sense of a clinical or image-based study. The performance data section refers to "software validation results." This suggests that the testing was primarily focused on the software's functionality, data integrity, and compliance with specifications, rather than a clinical trial with patient data.

• Sample Size for Test Set: Not specified, but likely refers to various data inputs and scenarios used during software validation.
• Data Provenance: Not specified. Given the nature of a software validation for a "Central Station," it's probable that various types of simulated or recorded vital signs data would have been used to test the system's ability to receive, display, and store. It's not a direct patient study with geographical or temporal data provenance.

3. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications

This information is not provided in the document. For a software system like the Central Station, "ground truth" would likely refer to the correctness of the data received, displayed, and stored, and the proper functioning of alerts based on predefined thresholds. The "experts" would likely be software testers, quality assurance personnel, and potentially clinical subject matter experts validating the accuracy of the displayed information against expected results. However, their numbers and specific qualifications are not mentioned.

4. Adjudication Method for the Test Set

This information is not provided in the document. Given that this is a software system for data display and storage, traditional adjudication methods for diagnostic images or clinical outcomes might not directly apply. The "adjudication" would likely be the verification of software outputs against expected behavior, potentially involving multiple testers or a structured review process, but no specific method is detailed.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

No, an MRMC comparative effectiveness study was not done. The device is a "Central Station" software system, not an AI-powered diagnostic tool that directly assists human readers in interpreting medical images or making clinical diagnoses. Its function is to display and store vital signs data for review by healthcare professionals.

6. Standalone (Algorithm Only Without Human-in-the-Loop Performance) Study

The entire study implicitly describes a standalone performance, as the "Central Station" software is the primary subject. The "software validation results demonstrated that the Central Station 3.5 System was in compliance... and that it performed within its specifications and functional requirements for software." This indicates a focus on the algorithm's direct functionality in processing and presenting data. However, it's crucial to understand that while the software operates standalone in processing, its intended use is for review and interpretation by a healthcare professional, meaning it's inherently part of a human-in-the-loop workflow.

7. Type of Ground Truth Used

The ground truth for this device would be defined by the correctness of software functionality and data integrity. This means:

• The system accurately receives data from specified patient monitors.
• The system accurately displays the vital signs parameters and related data.
• The system accurately stores the data.
• System alerts function correctly based on intended logic.
• The software complies with established guidelines and standards.

This is fundamentally a functional and performance validation against predefined software requirements and industry standards, rather than a clinical ground truth like pathology or patient outcomes.

8. Sample Size for the Training Set

This information is not applicable/not provided. The Honeywell HomMed Central Station is described as a software system that receives, displays, and stores vital signs data. It is not an AI/ML device that requires a "training set" in the context of machine learning model development. The software's logic and functionality are likely rule-based or algorithmic, programmed to perform specific tasks, rather than learned from a dataset.

9. How the Ground Truth for the Training Set Was Established

This information is not applicable as there is no "training set" for this type of software device.

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The HomMed Central Station is intended to be used to retrospectively receive, display, evaluate, analyze and store certain monitored physiological parameters of patients within healthcare and home environments. The physiologic patient parameters available for retrospective display and evaluation include NIBP, pulse rate, SpO2, temperature and weight. The Central Station will be the device used by the healthcare professional to display and evaluate the monitored patient data, and the healthcare professional is responsible for the interpretation of the monitored data made available by the Observer. The Central Station data will be a utilized by the healthcare professional health including physicians and/or physician supervised nurses. Physiological data, system alerts and patient data analysis will be available to the health care provider from Central Station that may be considered as a retrospective data monitor system.

The HomMed Central Station is a software system that operates on a commercially available PC system with the minimum performance specifications consistent with typical PC hardware and equipment specifications. The HomMed Central Station is a software system that is an accessory to the HomMed Sentry Patient Monitors.

Here's a breakdown of the acceptance criteria and study information for the HomMed Central Station, based on the provided text:

Summary of Acceptance Criteria and Reported Device Performance

• Medical Software Validation Standards
• EN 60601-1 Medical Electrical Safety
• EMC Compliance IEC 601-1-2
• ISO 10993-5, 10-11 Biocompatibility (Though Biocompatibility might be for associated hardware, not directly the software, it's listed under compliance). | "Software validation results demonstrated that the HomMed Central Station System was in compliance with the guidelines and standards referenced in the FDA reviewer's guides..." |
  | Safety | General safety requirements for medical electrical systems. | "Software validation results demonstrated that the HomMed Central Station System was in compliance with the guidelines and standards referenced in the FDA reviewer's guides..." (Likely covers safety implicitly). |

Study Information

Based on the provided text, the submission is a 510(k) Premarket Notification for the HomMed Central Station. This type of submission primarily focuses on demonstrating substantial equivalence to a legally marketed predicate device, rather than conducting extensive clinical efficacy trials typically seen for PMA applications.

Here's what can be inferred about the "study" mentioned for this device:

1. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective):

   • The document states: "The software validation results demonstrated that the HomMed Central Station System was in compliance with the guidelines and standards referenced in the FDA reviewer's guides and that it performed within its specifications and functional requirements."
   • This indicates a software validation study was performed. However, no specific sample size for a "test set" of patient data is provided. The validation would likely involve testing various scenarios and inputs to ensure the software (Central Station) accurately receives, displays, evaluates, analyzes, and stores the physiological parameters from the HomMed Sentry Patient Monitors.
   • Data provenance is not specified. Given the nature of a software validation, it would likely involve simulated or representative data, potentially from a variety of sources but not explicitly stated. The intent is to process data acquired "within healthcare and home environments."

2. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience):

   • The document does not mention the use of experts to establish ground truth for a test set in the context of validating algorithmic performance. The validation appears to be centered on the software's functional correctness and adherence to technical specifications. The "healthcare professional is responsible for the interpretation of the monitored data."

3. Adjudication method (e.g. 2+1, 3+1, none) for the test set:

   • Not applicable/Not mentioned. The study described is a software validation, not a clinical study involving adjudication of interpretations.

4. If a multi reader multi case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance:

   • No, an MRMC comparative effectiveness study was not done. The HomMed Central Station is described as "a retrospective data view and analysis system" and an "accessory device used by the healthcare professional to display and evaluate the monitored patient data." It does not claim to incorporate AI or provide interpretations that would necessitate an MRMC study comparing human performance with and without AI assistance. The healthcare professional is explicitly stated to be "responsible for the interpretation of the monitored data."

5. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:

   • Yes, in essence, the "software validation" serves as a standalone performance assessment of the algorithm/software's functionality. The validation assesses if the software, in isolation, correctly receives, processes, stores, and displays data according to its specifications. However, it's crucial to note that this "standalone" performance is within the context of a retrospective display and analysis system, where final interpretation remains with the human professional. The device is not making diagnostic decisions on its own.

6. The type of ground truth used (expert consensus, pathology, outcomes data, etc.):

   • For a software validation of this nature, the "ground truth" would likely be derived from:
     • Expected system behavior: Comparison of the software's output against predefined functional and performance requirements.
     • Input data accuracy: Ensuring that known input physiological data is accurately processed and displayed by the system.
     • Compliance with standards: Verifying that the software adheres to established software development and medical device standards.
   • There is no mention of pathology, expert consensus on patient conditions, or outcomes data being used as ground truth for this software validation.

7. The sample size for the training set:

   • Not applicable/Not mentioned. This device is not described as an "AI" or machine learning device that requires a training set in the typical sense. It is a data management and display software system.

8. How the ground truth for the training set was established:

   • Not applicable, as there is no indication of a "training set" for AI/ML processes.

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